Interactive data-centric viewpoint selection

Abstract

We propose a new algorithm for automatic viewpoint selection for volume data sets. While most previous algorithms depend on information theoretic frameworks, our algorithm solely focuses on the data itself without off-line rendering steps, and finds a view direction which shows the data set's features well. The algorithm consists of two main steps: feature selection and viewpoint selection. The feature selection step is an extension of the 2D Harris interest point detection algorithm. This step selects corner and/or high-intensity points as features, which captures the overall structures and local details. The second step, viewpoint selection, takes this set and finds a direction that lays out those points in a way that the variance of projected points is maximized, which can be formulated as a Principal Component Analysis (PCA) problem. The PCA solution guarantees that surfaces with detected corner points are less likely to be degenerative, and it minimizes occlusion between them. Our entire algorithm takes less than a second, which allows it to be integrated into real-time volume rendering applications where users can modify the volume with transfer functions, because the optimized viewpoint depends on the transfer function.